BOS (Beginning of Sequence) Token is a special token that marks the start of an input sequence in transformer models — providing a consistent initial context that enables the model to recognize sequence boundaries, initialize its hidden state from a known starting point, and distinguish between multiple independent sequences within the same batch, with different model families using different BOS conventions ([CLS] in BERT, in LLaMA, <|endoftext|> in GPT-2).

What Is the BOS Token?

• Definition: A reserved token in the model's vocabulary that is prepended to every input sequence — it occupies position 0 in the sequence, receives the first positional embedding, and serves as an explicit signal that a new sequence is beginning.
• Sequence Boundary: In batched processing where multiple sequences are packed together, the BOS token tells the model where one sequence ends and another begins — without it, the model cannot distinguish between a continuation of the previous sequence and the start of a new one.
• Initial Context: The BOS token provides a consistent, learned starting representation — the model's first attention computation has a known anchor point rather than starting from an arbitrary token.
• Classification Token: In encoder models like BERT, the BOS token ([CLS]) serves double duty — its final hidden state is used as the sequence-level representation for classification tasks (sentiment, NLI, similarity).

BOS Tokens Across Model Families

BOS Token Functions

• Sequence Initialization: Provides the first position embedding and initial attention anchor — the model learns what "the beginning of a sequence looks like" during training.
• Batch Boundary Detection: In continuous batching and packed sequences, BOS tokens mark where new sequences start — critical for attention masking to prevent cross-sequence attention leakage.
• Classification Pooling: In BERT-style models, the [CLS] token's final representation aggregates information from the entire sequence through self-attention — used as input to classification heads.
• Chat Template Markers: In chat models, BOS-like tokens (<|im_start|>, [INST]) mark the beginning of each message turn — enabling the model to distinguish between system, user, and assistant messages.

BOS tokens are the explicit sequence initialization markers that transformer models depend on for boundary detection and consistent starting context — a small but essential piece of the tokenization protocol that ensures models correctly process the beginning of every input sequence across batched inference, chat conversations, and classification tasks.